The Internet of Medical Things (IoMT) transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention. This innovative method revolutionizes healthcare by facilitating early disease detection and tailored care, particularly in chronic disease management, where IoMT automates treatments based on real-time health data collection. Nonetheless, its benefits are countered by significant security challenges that endanger the lives of its users due to the sensitivity and value of the processed data, thereby attracting malicious interests. Moreover, the utilization of wireless communication for data transmission exposes medical data to interception and tampering by cybercriminals. Additionally, anomalies may arise due to human errors, network interference, or hardware malfunctions. In this context, anomaly detection based on Machine Learning (ML) is an interesting solution, but it comes up against obstacles in terms of explicability and protection of privacy. To address these challenges, a new framework for Intrusion Detection Systems (IDS) is introduced, leveraging Artificial Neural Networks (ANN) for intrusion detection while utilizing Federated Learning (FL) for privacy preservation. Additionally, eXplainable Artificial Intelligence (XAI) methods are incorporated to enhance model explanation and interpretation. The efficacy of the proposed framework is evaluated and compared with centralized approaches using multiple datasets containing network and medical data, simulating various attack types impacting the confidentiality, integrity, and availability of medical and physiological data. The results obtained offer compelling evidence that the FL method performs comparably to the centralized method, demonstrating high performance. Additionally, it affords the dual advantage of safeguarding privacy and providing model explanation.

翻译：医疗物联网（IoMT）超越了传统医疗边界，推动医疗模式从被动治疗向主动预防转变。这一创新方法通过支持早期疾病检测和个性化护理，彻底改变了医疗保健领域，尤其在慢性病管理中，IoMT基于实时健康数据采集实现自动化治疗。然而，其优势也因处理数据的敏感性和价值而面临重大安全挑战，这些挑战威胁用户生命安全并吸引恶意攻击。此外，利用无线通信传输数据使得医疗数据面临被网络犯罪分子截获和篡改的风险。同时，人为错误、网络干扰或硬件故障也可能引发异常。在此背景下，基于机器学习（ML）的异常检测虽是一种有效解决方案，但在可解释性和隐私保护方面仍存在障碍。为应对这些挑战，本文提出了一种新的入侵检测系统（IDS）框架，利用人工神经网络（ANN）进行入侵检测，同时采用联邦学习（FL）保护隐私。此外，框架集成了可解释人工智能（XAI）方法以增强模型解释和可理解性。通过使用包含网络数据和医疗数据的多个数据集，模拟影响医疗与生理数据机密性、完整性和可用性的多种攻击类型，评估了所提框架的有效性，并与集中式方法进行了比较。实验结果表明，联邦学习方法在性能上与集中式方法相当，并兼具隐私保护与模型解释的双重优势。